enh: property type (#102)

README.md

@@ -68,13 +68,24 @@ print(properties.head())
 ```
 Required
 ├── location (str): The address in various formats - this could be just a zip code, a full address, or city/state, etc.
-└── listing_type (option): Choose the type of listing.
+├── listing_type (option): Choose the type of listing.
     - 'for_rent'
     - 'for_sale'
     - 'sold'
-    - 'pending'
+    - 'pending' (for pending/contingent sales)
 
 Optional
+├── property_type (list): Choose the type of properties.
+    - 'single_family'
+    - 'multi_family'
+    - 'condos'
+    - 'condo_townhome_rowhome_coop'
+    - 'condo_townhome'
+    - 'townhomes'
+    - 'duplex_triplex'
+    - 'farm'
+    - 'land'
+    - 'mobile'
 ├── radius (decimal): Radius in miles to find comparable properties based on individual addresses.
 │    Example: 5.5 (fetches properties within a 5.5-mile radius if location is set to a specific address; otherwise, ignored)
 │
@@ -94,7 +105,7 @@ Optional
 │
 ├── extra_property_data (True/False): Increases requests by O(n). If set, this fetches additional property data for general searches (e.g. schools, tax appraisals etc.)
 │
-├── exclude_pending (True/False): If set, excludes pending properties from the results unless listing_type is 'pending'
+├── exclude_pending (True/False): If set, excludes 'pending' properties from the 'for_sale' results unless listing_type is 'pending'
 │
 └── limit (integer): Limit the number of properties to fetch. Max & default is 10000.
 ```

examples/HomeHarvest_Demo.ipynb

@@ -1,141 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cb48903e-5021-49fe-9688-45cd0bc05d0f",
-   "metadata": {
-    "is_executing": true
-   },
-   "outputs": [],
-   "source": [
-    "from homeharvest import scrape_property\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "156488ce-0d5f-43c5-87f4-c33e9c427860",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('display.max_columns', None)  # Show all columns\n",
-    "pd.set_option('display.max_rows', None)     # Show all rows\n",
-    "pd.set_option('display.width', None)        # Auto-adjust display width to fit console\n",
-    "pd.set_option('display.max_colwidth', 50)   # Limit max column width to 50 characters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1c8b9744-8606-4e9b-8add-b90371a249a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check for sale properties\n",
-    "scrape_property(\n",
-    "    location=\"dallas\",\n",
-    "    listing_type=\"for_sale\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aaf86093",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# search a specific address\n",
-    "scrape_property(\n",
-    "    location=\"2530 Al Lipscomb Way\",\n",
-    "    listing_type=\"for_sale\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ab7b4c21-da1d-4713-9df4-d7425d8ce21e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check rentals\n",
-    "scrape_property(\n",
-    "    location=\"chicago, illinois\",\n",
-    "    listing_type=\"for_rent\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "af280cd3",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# check sold properties\n",
-    "properties = scrape_property(\n",
-    "    location=\"90210\",\n",
-    "    listing_type=\"sold\",\n",
-    "    past_days=10\n",
-    ")\n",
-    "display(properties)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "628c1ce2",
-   "metadata": {
-    "collapsed": false,
-    "is_executing": true,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# display clickable URLs\n",
-    "from IPython.display import display, HTML\n",
-    "properties['property_url'] = '<a href=\"' + properties['property_url'] + '\" target=\"_blank\">' + properties['property_url'] + '</a>'\n",
-    "\n",
-    "html = properties.to_html(escape=False)\n",
-    "truncate_width = f'<style>.dataframe td {{ max-width: 200px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }}</style>{html}'\n",
-    "display(HTML(truncate_width))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/HomeHarvest_Demo.py

@@ -1,20 +0,0 @@
-from homeharvest import scrape_property
-from datetime import datetime
-
-# Generate filename based on current timestamp
-current_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-filename = f"HomeHarvest_{current_timestamp}.csv"
-
-properties = scrape_property(
-    location="San Diego, CA",
-    listing_type="sold",  # or (for_sale, for_rent)
-    past_days=30,  # sold in last 30 days - listed in last x days if (for_sale, for_rent)
-    # pending_or_contingent=True # use on for_sale listings to find pending / contingent listings
-    # mls_only=True,  # only fetch MLS listings
-    # proxy="http://user:pass@host:port"  # use a proxy to change your IP address
-)
-print(f"Number of properties: {len(properties)}")
-
-# Export to csv
-properties.to_csv(filename, index=False)
-print(properties.head())

examples/price_of_land.py

@@ -0,0 +1,104 @@
+"""
+This script scrapes sold and pending sold land listings in past year for a list of zip codes and saves the data to individual Excel files.
+It adds two columns to the data: 'lot_acres' and 'ppa' (price per acre) for user to analyze average price of land in a zip code.
+"""
+
+import os
+import pandas as pd
+from homeharvest import scrape_property
+
+
+def get_property_details(zip: str, listing_type):
+    properties = scrape_property(location=zip, listing_type=listing_type, property_type=["land"], past_days=365)
+    if not properties.empty:
+        properties["lot_acres"] = properties["lot_sqft"].apply(lambda x: x / 43560 if pd.notnull(x) else None)
+
+        properties = properties[properties["sqft"].isnull()]
+        properties["ppa"] = properties.apply(
+            lambda row: (
+                int(
+                    (
+                        row["sold_price"]
+                        if (pd.notnull(row["sold_price"]) and row["status"] == "SOLD")
+                        else row["list_price"]
+                    )
+                    / row["lot_acres"]
+                )
+                if pd.notnull(row["lot_acres"])
+                and row["lot_acres"] > 0
+                and (pd.notnull(row["sold_price"]) or pd.notnull(row["list_price"]))
+                else None
+            ),
+            axis=1,
+        )
+        properties["ppa"] = properties["ppa"].astype("Int64")
+        selected_columns = [
+            "property_url",
+            "property_id",
+            "style",
+            "status",
+            "street",
+            "city",
+            "state",
+            "zip_code",
+            "county",
+            "list_date",
+            "last_sold_date",
+            "list_price",
+            "sold_price",
+            "lot_sqft",
+            "lot_acres",
+            "ppa",
+        ]
+        properties = properties[selected_columns]
+    return properties
+
+
+def output_to_excel(zip_code, sold_df, pending_df):
+    root_folder = os.getcwd()
+    zip_folder = os.path.join(root_folder, "zips", zip_code)
+
+    # Create zip code folder if it doesn't exist
+    os.makedirs(zip_folder, exist_ok=True)
+
+    # Define file paths
+    sold_file = os.path.join(zip_folder, f"{zip_code}_sold.xlsx")
+    pending_file = os.path.join(zip_folder, f"{zip_code}_pending.xlsx")
+
+    # Save individual sold and pending files
+    sold_df.to_excel(sold_file, index=False)
+    pending_df.to_excel(pending_file, index=False)
+
+
+zip_codes = map(
+    str,
+    [
+        22920,
+        77024,
+        78028,
+        24553,
+        22967,
+        22971,
+        22922,
+        22958,
+        22969,
+        22949,
+        22938,
+        24599,
+        24562,
+        22976,
+        24464,
+        22964,
+        24581,
+    ],
+)
+
+combined_df = pd.DataFrame()
+for zip in zip_codes:
+    sold_df = get_property_details(zip, "sold")
+    pending_df = get_property_details(zip, "pending")
+    combined_df = pd.concat([combined_df, sold_df, pending_df], ignore_index=True)
+    output_to_excel(zip, sold_df, pending_df)
+
+combined_file = os.path.join(os.getcwd(), "zips", "combined.xlsx")
+combined_df.to_excel(combined_file, index=False)

homeharvest/__init__.py

@@ -3,12 +3,13 @@ import pandas as pd
 from .core.scrapers import ScraperInput
 from .utils import process_result, ordered_properties, validate_input, validate_dates, validate_limit
 from .core.scrapers.realtor import RealtorScraper
-from .core.scrapers.models import ListingType
+from .core.scrapers.models import ListingType, SearchPropertyType
 
 
 def scrape_property(
     location: str,
     listing_type: str = "for_sale",
+    property_type: list[str] | None = None,
     radius: float = None,
     mls_only: bool = False,
     past_days: int = None,
@@ -24,6 +25,7 @@ def scrape_property(
     Scrape properties from Realtor.com based on a given location and listing type.
     :param location: Location to search (e.g. "Dallas, TX", "85281", "2530 Al Lipscomb Way")
     :param listing_type: Listing Type (for_sale, for_rent, sold, pending)
+    :param property_type: Property Type (single_family, multi_family, condos, condo_townhome_rowhome_coop, condo_townhome, townhomes, duplex_triplex, farm, land, mobile)
     :param radius: Get properties within _ (e.g. 1.0) miles. Only applicable for individual addresses.
     :param mls_only: If set, fetches only listings with MLS IDs.
     :param proxy: Proxy to use for scraping
@@ -41,6 +43,7 @@ def scrape_property(
     scraper_input = ScraperInput(
         location=location,
         listing_type=ListingType[listing_type.upper()],
+        property_type=[SearchPropertyType[prop.upper()] for prop in property_type] if property_type else None,
         proxy=proxy,
         radius=radius,
         mls_only=mls_only,
@@ -63,4 +66,6 @@ def scrape_property(
     with warnings.catch_warnings():
         warnings.simplefilter("ignore", category=FutureWarning)
 
-        return pd.concat(properties_dfs, ignore_index=True, axis=0)[ordered_properties].replace({"None": pd.NA, None: pd.NA, "": pd.NA})
+        return pd.concat(properties_dfs, ignore_index=True, axis=0)[ordered_properties].replace(
+            {"None": pd.NA, None: pd.NA, "": pd.NA}
+        )

homeharvest/core/scrapers/__init__.py

@@ -5,7 +5,7 @@ from requests.adapters import HTTPAdapter
 from urllib3.util.retry import Retry
 import uuid
 from ...exceptions import AuthenticationError
-from .models import Property, ListingType, SiteName
+from .models import Property, ListingType, SiteName, SearchPropertyType
 import json
 
 
@@ -13,6 +13,7 @@ import json
 class ScraperInput:
     location: str
     listing_type: ListingType
+    property_type: list[SearchPropertyType] | None = None
     radius: float | None = None
     mls_only: bool | None = False
     proxy: str | None = None
@@ -34,11 +35,12 @@ class Scraper:
     ):
         self.location = scraper_input.location
         self.listing_type = scraper_input.listing_type
+        self.property_type = scraper_input.property_type
 
         if not self.session:
             Scraper.session = requests.Session()
             retries = Retry(
-                total=3, backoff_factor=3, status_forcelist=[429, 403], allowed_methods=frozenset(["GET", "POST"])
+                total=3, backoff_factor=4, status_forcelist=[429, 403], allowed_methods=frozenset(["GET", "POST"])
             )
 
             adapter = HTTPAdapter(max_retries=retries)
@@ -46,21 +48,21 @@ class Scraper:
             Scraper.session.mount("https://", adapter)
             Scraper.session.headers.update(
                 {
-                    'accept': 'application/json, text/javascript',
+                    "accept": "application/json, text/javascript",
-                    'accept-language': 'en-US,en;q=0.9',
+                    "accept-language": "en-US,en;q=0.9",
-                    'cache-control': 'no-cache',
+                    "cache-control": "no-cache",
-                    'content-type': 'application/json',
+                    "content-type": "application/json",
-                    'origin': 'https://www.realtor.com',
+                    "origin": "https://www.realtor.com",
-                    'pragma': 'no-cache',
+                    "pragma": "no-cache",
-                    'priority': 'u=1, i',
+                    "priority": "u=1, i",
-                    'rdc-ab-tests': 'commute_travel_time_variation:v1',
+                    "rdc-ab-tests": "commute_travel_time_variation:v1",
-                    'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
+                    "sec-ch-ua": '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
-                    'sec-ch-ua-mobile': '?0',
+                    "sec-ch-ua-mobile": "?0",
-                    'sec-ch-ua-platform': '"Windows"',
+                    "sec-ch-ua-platform": '"Windows"',
-                    'sec-fetch-dest': 'empty',
+                    "sec-fetch-dest": "empty",
-                    'sec-fetch-mode': 'cors',
+                    "sec-fetch-mode": "cors",
-                    'sec-fetch-site': 'same-origin',
+                    "sec-fetch-site": "same-origin",
-                    'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
+                    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36",
                 }
             )
 
@@ -94,27 +96,29 @@ class Scraper:
         response = requests.post(
             "https://graph.realtor.com/auth/token",
             headers={
-                'Host': 'graph.realtor.com',
+                "Host": "graph.realtor.com",
-                'Accept': '*/*',
+                "Accept": "*/*",
-                'Content-Type': 'Application/json',
+                "Content-Type": "Application/json",
-                'X-Client-ID': 'rdc_mobile_native,iphone',
+                "X-Client-ID": "rdc_mobile_native,iphone",
-                'X-Visitor-ID': device_id,
+                "X-Visitor-ID": device_id,
-                'X-Client-Version': '24.21.23.679885',
+                "X-Client-Version": "24.21.23.679885",
-                'Accept-Language': 'en-US,en;q=0.9',
+                "Accept-Language": "en-US,en;q=0.9",
-                'User-Agent': 'Realtor.com/24.21.23.679885 CFNetwork/1494.0.7 Darwin/23.4.0',
+                "User-Agent": "Realtor.com/24.21.23.679885 CFNetwork/1494.0.7 Darwin/23.4.0",
             },
-            data=json.dumps({
+            data=json.dumps(
+                {
                     "grant_type": "device_mobile",
                     "device_id": device_id,
-                "client_app_id": "rdc_mobile_native,24.21.23.679885,iphone"
+                    "client_app_id": "rdc_mobile_native,24.21.23.679885,iphone",
-            }))
+                }
+            ),
+        )
 
         data = response.json()
 
         if not (access_token := data.get("access_token")):
             raise AuthenticationError(
-                "Failed to get access token, use a proxy/vpn or wait a moment and try again.",
+                "Failed to get access token, use a proxy/vpn or wait a moment and try again.", response=response
-                response=response
             )
 
         return access_token

homeharvest/core/scrapers/models.py

@@ -17,6 +17,19 @@ class SiteName(Enum):
         raise ValueError(f"{value} not found in {cls}")
 
 
+class SearchPropertyType(Enum):
+    SINGLE_FAMILY = "single_family"
+    CONDOS = "condos"
+    CONDO_TOWNHOME_ROWHOME_COOP = "condo_townhome_rowhome_coop"
+    CONDO_TOWNHOME = "condo_townhome"
+    TOWNHOMES = "townhomes"
+    DUPLEX_TRIPLEX = "duplex_triplex"
+    FARM = "farm"
+    LAND = "land"
+    MULTI_FAMILY = "multi_family"
+    MOBILE = "mobile"
+
+
 class ListingType(Enum):
     FOR_SALE = "FOR_SALE"
     FOR_RENT = "FOR_RENT"

homeharvest/core/scrapers/realtor/__init__.py

@@ -6,12 +6,28 @@ This module implements the scraper for realtor.com
 """
 
 from __future__ import annotations
+
+import json
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from datetime import datetime
+from json import JSONDecodeError
 from typing import Dict, Union, Optional
 
+from tenacity import retry, retry_if_exception_type, wait_exponential, stop_after_attempt
+
 from .. import Scraper
-from ..models import Property, Address, ListingType, Description, PropertyType, Agent, Broker, Builder, Advertisers, Office
+from ..models import (
+    Property,
+    Address,
+    ListingType,
+    Description,
+    PropertyType,
+    Agent,
+    Broker,
+    Builder,
+    Advertisers,
+    Office,
+)
 from .queries import GENERAL_RESULTS_QUERY, SEARCH_HOMES_DATA, HOMES_DATA
 
 
@@ -81,9 +97,12 @@ class RealtorScraper(Scraper):
             return property_info["listings"][0]["listing_id"]
 
     def handle_home(self, property_id: str) -> list[Property]:
-        query = """query Home($property_id: ID!) {
+        query = (
+            """query Home($property_id: ID!) {
                     home(property_id: $property_id) %s
-                }""" % HOMES_DATA
+                }"""
+            % HOMES_DATA
+        )
 
         variables = {"property_id": property_id}
         payload = {
@@ -96,9 +115,7 @@ class RealtorScraper(Scraper):
 
         property_info = response_json["data"]["home"]
 
-        return [
+        return [self.process_property(property_info, "home")]
-            self.process_property(property_info, "home")
-        ]
 
     @staticmethod
     def process_advertisers(advertisers: list[dict] | None) -> Advertisers | None:
@@ -122,7 +139,7 @@ class RealtorScraper(Scraper):
                     phones=advertiser.get("phones"),
                 )
 
-                if advertiser.get('broker') and advertiser["broker"].get('name'):  #: has a broker
+                if advertiser.get("broker") and advertiser["broker"].get("name"):  #: has a broker
                     processed_advertisers.broker = Broker(
                         uuid=_parse_fulfillment_id(advertiser["broker"].get("fulfillment_id")),
                         name=advertiser["broker"].get("name"),
@@ -159,9 +176,10 @@ class RealtorScraper(Scraper):
             and result["location"]["address"].get("coordinate")
         )
 
-        is_pending = result["flags"].get("is_pending") or result["flags"].get("is_contingent")
+        is_pending = result["flags"].get("is_pending")
+        is_contingent = result["flags"].get("is_contingent")
 
-        if is_pending and (self.exclude_pending and self.listing_type != ListingType.PENDING):
+        if (is_pending or is_contingent) and (self.exclude_pending and self.listing_type != ListingType.PENDING):
             return
 
         property_id = result["property_id"]
@@ -184,7 +202,7 @@ class RealtorScraper(Scraper):
             property_url=result["href"],
             property_id=property_id,
             listing_id=result.get("listing_id"),
-            status="PENDING" if is_pending else result["status"].upper(),
+            status="PENDING" if is_pending else "CONTINGENT" if is_contingent else result["status"].upper(),
             list_price=result["list_price"],
             list_price_min=result["list_price_min"],
             list_price_max=result["list_price_max"],
@@ -225,6 +243,11 @@ class RealtorScraper(Scraper):
             elif self.last_x_days:
                 date_param = f'list_date: {{ min: "$today-{self.last_x_days}D" }}'
 
+        property_type_param = ""
+        if self.property_type:
+            property_types = [pt.value for pt in self.property_type]
+            property_type_param = f"type: {json.dumps(property_types)}"
+
         sort_param = (
             "sort: [{ field: sold_date, direction: desc }]"
             if self.listing_type == ListingType.SOLD
@@ -259,6 +282,7 @@ class RealtorScraper(Scraper):
                                 status: %s
                                 %s
                                 %s
+                                %s
                             }
                             %s
                             limit: 200
@@ -268,6 +292,7 @@ class RealtorScraper(Scraper):
                 is_foreclosure,
                 listing_type.value.lower(),
                 date_param,
+                property_type_param,
                 pending_or_contingent_param,
                 sort_param,
                 GENERAL_RESULTS_QUERY,
@@ -290,6 +315,7 @@ class RealtorScraper(Scraper):
                                         status: %s
                                         %s
                                         %s
+                                        %s
                                     }
                                     %s
                                     limit: 200
@@ -299,6 +325,7 @@ class RealtorScraper(Scraper):
                 is_foreclosure,
                 listing_type.value.lower(),
                 date_param,
+                property_type_param,
                 pending_or_contingent_param,
                 sort_param,
                 GENERAL_RESULTS_QUERY,
@@ -350,9 +377,7 @@ class RealtorScraper(Scraper):
         properties_list = properties_list[: self.limit - offset]
 
         with ThreadPoolExecutor(max_workers=self.NUM_PROPERTY_WORKERS) as executor:
-            futures = [
+            futures = [executor.submit(self.process_property, result, search_key) for result in properties_list]
-                executor.submit(self.process_property, result, search_key) for result in properties_list
-            ]
 
             for future in as_completed(futures):
                 result = future.result()
@@ -451,6 +476,9 @@ class RealtorScraper(Scraper):
             "assessed_value": assessed_value if assessed_value else None,
         }
 
+    @retry(
+        retry=retry_if_exception_type(JSONDecodeError), wait=wait_exponential(min=4, max=10), stop=stop_after_attempt(3)
+    )
     def get_prop_details(self, property_id: str) -> dict:
         if not self.extra_property_data:
             return {}
@@ -534,7 +562,9 @@ class RealtorScraper(Scraper):
             style = style.upper()
 
         primary_photo = ""
-        if (primary_photo_info := result.get('primary_photo')) and (primary_photo_href := primary_photo_info.get("href")):
+        if (primary_photo_info := result.get("primary_photo")) and (
+            primary_photo_href := primary_photo_info.get("href")
+        ):
             primary_photo = primary_photo_href.replace("s.jpg", "od-w480_h360_x2.webp?w=1080&q=75")
 
         return Description(
@@ -547,7 +577,7 @@ class RealtorScraper(Scraper):
             sqft=description_data.get("sqft"),
             lot_sqft=description_data.get("lot_sqft"),
             sold_price=(
-                result.get('last_sold_price') or description_data.get("sold_price")
+                result.get("last_sold_price") or description_data.get("sold_price")
                 if result.get("last_sold_date") or result["list_price"] != description_data.get("sold_price")
                 else None
             ),  #: has a sold date or list and sold price are different
@@ -581,4 +611,8 @@ class RealtorScraper(Scraper):
         if not photos_info:
             return None
 
-        return [photo_info["href"].replace("s.jpg", "od-w480_h360_x2.webp?w=1080&q=75") for photo_info in photos_info if photo_info.get("href")]
+        return [
+            photo_info["href"].replace("s.jpg", "od-w480_h360_x2.webp?w=1080&q=75")
+            for photo_info in photos_info
+            if photo_info.get("href")
+        ]

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "annotated-types"
@@ -667,6 +667,21 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
+[[package]]
+name = "tenacity"
+version = "9.0.0"
+description = "Retry code until it succeeds"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"},
+    {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"},
+]
+
+[package.extras]
+doc = ["reno", "sphinx"]
+test = ["pytest", "tornado (>=4.5)", "typeguard"]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -740,4 +755,4 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "21ef9cfb35c446a375a2b74c37691d7031afb1e4f66a8b63cb7c1669470689d2"
+content-hash = "cefc11b1bf5ad99d628f6d08f6f03003522cc1b6e48b519230d99d716a5c165c"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "homeharvest"
-version = "0.4.3"
+version = "0.4.4"
 description = "Real estate scraping library"
 authors = ["Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>"]
 homepage = "https://github.com/Bunsly/HomeHarvest"
@@ -14,6 +14,7 @@ python = ">=3.9,<3.13"
 requests = "^2.31.0"
 pandas = "^2.1.1"
 pydantic = "^2.7.4"
+tenacity = "^9.0.0"
 
 
 [tool.poetry.group.dev.dependencies]

tests/test_realtor.py

@@ -105,8 +105,12 @@ def test_realtor():
             location="2530 Al Lipscomb Way",
             listing_type="for_sale",
         ),
-        scrape_property(location="Phoenix, AZ", listing_type="for_rent", limit=1000),  #: does not support "city, state, USA" format
+        scrape_property(
-        scrape_property(location="Dallas, TX", listing_type="sold", limit=1000),  #: does not support "city, state, USA" format
+            location="Phoenix, AZ", listing_type="for_rent", limit=1000
+        ),  #: does not support "city, state, USA" format
+        scrape_property(
+            location="Dallas, TX", listing_type="sold", limit=1000
+        ),  #: does not support "city, state, USA" format
         scrape_property(location="85281"),
     ]
 
@@ -114,11 +118,13 @@ def test_realtor():
 
 
 def test_realtor_city():
-    results = scrape_property(
+    results = scrape_property(location="Atlanta, GA", listing_type="for_sale", limit=1000)
-        location="Atlanta, GA",
+
-        listing_type="for_sale",
+    assert results is not None and len(results) > 0
-        limit=1000
+
-    )
+
+def test_realtor_land():
+    results = scrape_property(location="Atlanta, GA", listing_type="for_sale", property_type=["land"], limit=1000)
 
     assert results is not None and len(results) > 0
 
@@ -241,9 +247,10 @@ def test_apartment_list_price():
     results = results[results["style"] == "APARTMENT"]
 
     #: get percentage of results with atleast 1 of any column not none, list_price, list_price_min, list_price_max
-    assert len(results[results[["list_price", "list_price_min", "list_price_max"]].notnull().any(axis=1)]) / len(
+    assert (
-        results
+        len(results[results[["list_price", "list_price_min", "list_price_max"]].notnull().any(axis=1)]) / len(results)
-    ) > 0.5
+        > 0.5
+    )
 
 
 def test_builder_exists():